Identifying and/or blocking ads such as document-specific competitive ads

ABSTRACT

A content owner partner (e.g., a Website/Web page publisher) can easily block entire broad or narrow categories of ads, and can specify objectionable ad content or targeting. Concepts may be associated with a property and ads related to those concepts may be blocked for the given property. Further, terms may be associated with a property and ads including any of the terms may be blocked for the given property.

§ 1. BACKGROUND OF THE INVENTION

§ 1.1 Field of the Invention

The present invention concerns advertising. In particular, the presentinvention concerns improving content-targeted advertising.

§ 1.2 Related Art

Advertising using traditional media, such as television, radio,newspapers and magazines, is well known. Unfortunately, even when armedwith demographic studies and entirely reasonable assumptions about thetypical audience of various media outlets, advertisers recognize thatmuch of their ad budget is simply wasted. Moreover, it is very difficultto identify and eliminate such waste.

Recently, advertising over more interactive media has become popular.For example, as the number of people using the Internet has exploded,advertisers have come to appreciate media and services offered over theInternet as a potentially powerful way to advertise.

Advertisers have developed several strategies in an attempt to maximizethe value of such advertising. In one strategy, advertisers use popularpresences or means for providing interactive media or services (referredto as “Websites” in the specification without loss of generality) asconduits to reach a large audience. Using this first approach, anadvertiser may place ads on the home page of the New York Times Website,or the USA Today Website, for example. In another strategy, anadvertiser may attempt to target its ads to more narrow niche audiences,thereby increasing the likelihood of a positive response by theaudience. For example, an agency promoting tourism in the Costa Ricanrainforest might place ads on the ecotourism-travel subdirectory of theYahoo Website. An advertiser will normally determine such targetingmanually.

Regardless of the strategy, Website-based ads (also referred to as “Webads”) are often presented to their advertising audience in the form of“banner ads”—i.e., a rectangular box that includes graphic components.When a member of the advertising audience (referred to as a “viewer” or“user” in the Specification without loss of generality) selects one ofthese banner ads by clicking on it, embedded hypertext links typicallydirect the viewer to the advertiser's Website. This process, wherein theviewer selects an ad, is commonly referred to as a “click-through”(“Click-through” is intended to cover any user selection.). The ratio ofthe number of click-throughs to the number of impressions of the ad(i.e., the number of times an ad is displayed or otherwise rendered) iscommonly referred to as the “click-through rate” or “CTR” of the ad.

A “conversion” is said to occur when a user consummates a transactionrelated to a previously served ad. What constitutes a conversion mayvary from case to case and can be determined in a variety of ways. Forexample, it may be the case that a conversion occurs when a user clickson an ad, is referred to the advertiser's Web page, and consummates apurchase there before leaving that Web page. Alternatively, a conversionmay be defined as a user being shown an ad, and making a purchase on theadvertiser's Web page within a predetermined time (e.g., seven days). Inyet another alternative, a conversion may be defined by an advertiser tobe any measurable/observable user action such as, for example,downloading a white paper, navigating to at least a given depth of aWebsite, viewing at least a certain number of Web pages, spending atleast a predetermined amount of time on a Website or Web page, etc.Often, if user actions don't indicate a consummated purchase, they mayindicate a sales lead, although user actions constituting a conversionare not limited to this. Indeed, many other definitions of whatconstitutes a conversion are possible. The ratio of the number ofconversions to the number of impressions of the ad (i.e., the number oftimes an ad is displayed or otherwise rendered) is commonly referred toas the conversion rate. If a conversion is defined to be able to occurwithin a predetermined time since the serving of an ad, one possibledefinition of the conversion rate might only consider ads that have beenserved more than the predetermined time in the past.

The hosts of Websites on which the ads are presented (referred to as“Website hosts” or “ad consumers”) have the challenge of maximizing adrevenue without impairing their users' experience. Some Website hostshave chosen to place advertising revenues over the interests of users.One such Website is “Overture.com,” which hosts a so-called “searchengine” service returning advertisements masquerading as “searchresults” in response to user queries. The Overture.com Website permitsadvertisers to pay to position an ad for their Website (or a targetWebsite) higher up on the list of purported search results. If suchschemes where the advertiser only pays if a user clicks on the ad (i.e.,cost-per-click) are implemented, the advertiser lacks incentive totarget their ads effectively, since a poorly targeted ad will not beclicked and therefore will not require payment. Consequently, highcost-per-click ads show up near or at the top, but do not necessarilytranslate into real revenue for the ad publisher because viewers don'tclick on them. Furthermore, ads that viewers would click on are furtherdown the list, or not on the list at all, and so relevancy of ads iscompromised.

Search engines, such as Google for example, have enabled advertisers totarget their ads so that they will be rendered in conjunction with asearch results page responsive to a query that is relevant, presumably,to the ad. The Google system tracks click-through statistics (which is aperformance parameter) for ads and keywords. Given a search keyword,there are a limited number of keyword targeted ads that could be shown,leading to a relatively manageable problem space. Although search resultpages afford advertisers a great opportunity to target their ads to amore receptive audience, search result pages are merely a fraction ofpage views of the World Wide Web.

Some online advertising systems may use ad relevance information anddocument content relevance information (e.g., concepts or topics,feature vectors, etc.) to “match” ads to (and/or to score ads withrespect to) a document including content, such as a Web page forexample. Examples of such online advertising systems are described in:

-   -   U.S. Provisional Application Ser. No. 60/413,536 (incorporated        herein by reference), entitled “METHODS AND APPARATUS FOR        SERVING RELEVANT ADVERTISEMENTS,” filed on Sep. 24, 2002 and        listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit as        inventors;    -   U.S. patent application Ser. No. 10/314,427 (incorporated herein        by reference), entitled “METHODS AND APPARATUS FOR SERVING        RELEVANT ADVERTISEMENTS,” filed on Dec. 6, 2002 and listing        Jeffrey A. Dean, Georges R. Harik and Paul Bucheit as inventors;    -   U.S. patent application Ser. No. 10/375,900 (incorporated herein        by reference), entitled “SERVING ADVERTISEMENTS BASED ON        CONTENT,” filed on Feb. 26, 2003 and listing Darrell Anderson,        Paul Bucheit, Alex Carobus, Claire Cui, Jeffrey A. Dean,        Georges R. Harik, Deepak Jindal, and Narayanan Shivakumar as        inventors; and    -   U.S. patent application Ser. No. 10/452,830 (incorporated herein        by reference), entitled “SERVING ADVERTISEMENTS USING        INFORMATION ASSOCIATED WITH E-MAIL,” filed on Jun. 2, 2003 and        listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit as        inventors.        Generally, such online advertising systems may use relevance        information of both candidate advertisements and a document to        determine a score of each ad relative to the document. The score        may be used to determine whether or not to serve an ad in        association with the document (also referred to as eligibility        determinations), and/or to determine a relative attribute (e.g.,        screen position, size, etc.) of one or more ads to be served in        association with the document. The determination of the score        may also use, for example, one or more of (1) one or more        performance parameters (e.g., click-through rate, conversion        rate, user ratings, etc.) of the ad, (2) quality information        about an advertiser associated with the ad, and (3) price        information (e.g., a maximum price per result (e.g., per click,        per conversion, per impression, etc.)) associated with the ad.

Many content owners (e.g., publishers of Web pages) who sell adinventory on their Websites (or otherwise agree to have ads rendered ontheir Websites) do not want to display ads that compete with theirproduct offerings. Some content owners have existing exclusiverelationships with advertisers. Such content owners either do not wantto display, or are contractually prohibited from displaying, ads thatcompete with their exclusive partner's product offerings. For example, aWebsite selling auto insurance may not want to show ads with links toother Websites selling auto insurance. Similarly, a Website with contentrelated to flowers may have an exclusive relationship with a flowerdelivery company to show only its ads for flower delivery.

Some ad serving systems offer a URL-based or domain-based (e.g., Websitebased) ad blocking. In such systems, a block list includes URLs and/orWebsite home pages. Ads may include a visible URL or a link to a URL. Ifan ad includes a visible URL or a link to a URL that is on the blocklist associated with a particular Web page, it is not served with thatWeb page. Unfortunately, generating block lists often entails a highlymanual process of generating related keywords and searching on thosekeywords to identify ads that should be blocked. Further, managing suchblock lists becomes difficult as new ads for new Web pages or Websitesare added. Otherwise, the block list will not block new ads enteredafter the initial creation of the block list. Finally, block lists areoften over-inclusive. For example, all ads on superstores like Amazonmight be blocked when only a product category needs to be blocked. Thus,potential advertising revenue is lost.

Some ad serving systems, particularly those that serve ads targeted toterms of a search query, allow content owners to use a list of keywords,commonly referred to as “black lists,” to black out ads or block ads fora set of search terms competitive to the content owner or its exclusivepartner. For example, America Online might want to block out adstargeted to the keyword “ISP.” Unfortunately, black lists do not workvery well for content-based ad targeting since a Web page may beassociated with multiple categories. Instead of eliminating all adstargeted to black listed keywords (e.g., flowers, roses, tulips,carnations, bouquet, baby's breath, . . . , or 1800access, USWest, JunoOnline, . . . ), which entails an extensive list of keywords, it's bestto just eliminate the ads for the offending category (e.g., flowers, orInternet service providers) and show other related ads. Thus, blacklists have the problem of requiring manually generating a set ofkeywords pertaining to a category. Since these lists are oftenunder-inclusive, particularly if they are not updated regularly,undesirable ads may be served on a content owner's document, resultingin lost good will. Indeed, this problem is more apparent content-basedad targeting partners than search-based keyword targeting partners,since ad slippage (i.e., the rendering of an ad that should be blocked)is visible on high traffic pages of a content site as opposed to adslippage on an esoteric search results page. Further, without carefulconsideration, a black list may be over-inclusive and block ads with anobjectionable keyword but in an adjacent category. For example, it maybe desired to block ads for Sony consumer electronics, but if “Sony” isadded to the blacklist, ads for Sony DVDs may be inadvertently blocked.

In view of the foregoing, there is a need for better ad blockingtechniques. Such techniques should meet one or more of the followinggoals: (i) be easy to set up; (ii) be easy to manage; (iii) avoidunder-inclusion; (iv) avoid over-inclusion; and (v) work withcontent-targeted ad serving systems.

§ 2. SUMMARY OF THE INVENTION

The present invention enables a content owner partner (e.g., aWebsite/Web page publisher) to easily block entire broad or narrowcategories of ads, and to specify objectionable ad content or targeting.The present invention may do so by associating concepts with a propertyand blocking ads related to those concepts for the given property,and/or associating terms with a property and blocking ads including anyof the terms for the given property.

§ 3. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level diagram showing parties or entities that caninteract with an advertising system.

FIG. 2 is a diagram illustrating an environment in which, or with which,the present invention may operate.

FIG. 3 is a bubble diagram of a first embodiment in which a set ofcandidate ads is filtered in a manner consistent with the presentinvention.

FIG. 4 is a bubble diagram of a second embodiment which blocks ads in amanner consistent with the present invention.

FIG. 5 is a flow diagram of an exemplary method 500 for performing broadad filtering in a manner consistent with the present invention.

FIG. 6 is a flow diagram of an exemplary method 600 for performingnarrow ad filtering in a manner consistent with the present invention.

FIG. 7 is a flow diagram of an exemplary method 700 for performing broadad blocking in a manner consistent with the present invention.

FIG. 8 illustrates an application of a broad ad filtering technique thatis consistent with the present invention.

FIG. 9 illustrates an application of a broad ad blocking technique thatis consistent with the present invention.

FIG. 10 illustrates an application of a specific ad filtering techniquethat is consistent with the present invention.

FIG. 11 is a block diagram of an exemplary apparatus that may performvarious operations in a manner consistent with the present invention.

§ 4. DETAILED DESCRIPTION

The present invention may involve novel methods, apparatus, messageformats and/or data structures for improving ad blocking, such as adblocking for use with a content-targeted ad serving system. Thefollowing description is presented to enable one skilled in the art tomake and use the invention, and is provided in the context of particularapplications and their requirements. Various modifications to thedisclosed embodiments will be apparent to those skilled in the art, andthe general principles set forth below may be applied to otherembodiments and applications. Thus, the present invention is notintended to be limited to the embodiments shown and the inventors regardtheir invention as any patentable subject matter described.

In the following, environments in which, or with which, the presentinvention may operate are described in § 4.1. Then, exemplaryembodiments of the present invention are described in § 4.2.Illustrative operations of exemplary embodiments of the presentinvention are then provided in § 4.3. Finally, some conclusionsregarding the present invention are set forth in § 4.4.

§ 4.1 Environments in which, or with which, the Present Invention mayOperate

§ 4.1.1 Exemplary Advertising Environment

FIG. 1 is a high level diagram of an advertising environment. Theenvironment may include an ad entry, maintenance and delivery system(simply referred to an ad server) 120. Advertisers 110 may directly, orindirectly, enter, maintain, and track ad information in the system 120.The ads may be in the form of graphical ads such as so-called bannerads, text only ads, image ads, audio ads, video ads, ads combining oneof more of any of such components, etc. The ads may also includeembedded information, such as a link, and/or machine executableinstructions. Ad consumers 130 may submit requests for ads to, acceptads responsive to their request from, and provide usage information to,the system 120. An entity other than an ad consumer 130 may initiate arequest for ads. Although not shown, other entities may provide usageinformation (e.g., whether or not a conversion or click-through relatedto the ad occurred) to the system 120. This usage information mayinclude measured or observed user behavior related to ads that have beenserved.

The ad server 120 may be similar to the one described in FIG. 2 of U.S.patent application Ser. No. 10/375,900, mentioned in § 1.2 above. Anadvertising program may include information concerning accounts,campaigns, creatives, targeting, etc. The term “account” relates toinformation for a given advertiser (e.g., a unique e-mail address, apassword, billing information, etc.). A “campaign” or “ad campaign”refers to one or more groups of one or more advertisements, and mayinclude a start date, an end date, budget information, geo-targetinginformation, syndication information, etc. For example, Honda may haveone advertising campaign for its automotive line, and a separateadvertising campaign for its motorcycle line. The campaign for itsautomotive line have one or more ad groups, each containing one or moreads. Each ad group may include targeting information (e.g., a set ofkeywords, a set of one or more topics, etc.), and price information(e.g., maximum cost (cost per click-though, cost per conversion, etc.)).Alternatively, or in addition, each ad group may include an average cost(e.g., average cost per click-through, average cost per conversion,etc.). Therefore, a single maximum cost and/or a single average cost maybe associated with one or more keywords, and/or topics. As stated, eachad group may have one or more ads or “creatives” (That is, ad contentthat is ultimately rendered to an end user.). Each ad may also include alink to a URL (e.g., a landing Web page, such as the home page of anadvertiser, or a Web page associated with a particular product orserver). Naturally, the ad information may include more or lessinformation, and may be organized in a number of different ways.

FIG. 2 illustrates an environment 200 in which the present invention maybe used. A user device (also referred to as a “client” or “clientdevice”) 250 may include a browser facility (such as the Explorerbrowser from Microsoft, the Opera Web Browser from Opera Software ofNorway, the Navigator browser from AOL/Time Warner, etc.), an e-mailfacility (e.g., Outlook from Microsoft), etc. A search engine 220 maypermit user devices 250 to search collections of documents (e.g., Webpages). A content server 210 may permit user devices 250 to accessdocuments. An e-mail server (such as Hotmail from Microsoft Network,Yahoo Mail, etc.) 240 may be used to provide e-mail functionality touser devices 250. An ad server 210 may be used to serve ads to userdevices 250. The ads may be served in association with search resultsprovided by the search engine 220. However, more relevant to the presentinvention, content-relevant ads may be served in association withcontent provided by the content server 230, and/or e-mail supported bythe e-mail server 240 and/or user device e-mail facilities.

As discussed in U.S. patent application Ser. No. 10/375,900 (introducedabove), ads may be targeted to documents served by content servers.Thus, one example of an ad consumer 130 is a general content server 230that receives requests for documents (e.g., articles, discussionthreads, music, video, graphics, search results, Web page listings,etc.), and retrieves the requested document in response to, or otherwiseservices, the request. The content server may submit a request for adsto the ad server 120/210. Such an ad request may include a number of adsdesired. The ad request may also include document request information.This information may include the document itself (e.g., page), acategory or topic corresponding to the content of the document or thedocument request (e.g., arts, business, computers, arts-movies,arts-music, etc.), part or all of the document request, content age,content type (e.g., text, graphics, video, audio, mixed media, etc.),geo-location information, document information, etc.

The content server 230 may combine the requested document with one ormore of the advertisements provided by the ad server 120/210. Thiscombined information including the document content and advertisement(s)is then forwarded towards the end user device 250 that requested thedocument, for presentation to the user. Finally, the content server 230may transmit information about the ads and how, when, and/or where theads are to be rendered (e.g., position, click-through or not, impressiontime, impression date, size, conversion or not, etc.) back to the adserver 120/210. Alternatively, or in addition, such information may beprovided back to the ad server 120/210 by some other means.

Another example of an ad consumer 130 is the search engine 220. A searchengine 220 may receive queries for search results. In response, thesearch engine may retrieve relevant search results (e.g., from an indexof Web pages). An exemplary search engine is described in the article S.Brin and L. Page, “The Anatomy of a Large-Scale Hypertextual SearchEngine,” Seventh International World Wide Web Conference, Brisbane,Australia and in U.S. Pat. No. 6,285,999 (both incorporated herein byreference). Such search results may include, for example, lists of Webpage titles, snippets of text extracted from those Web pages, andhypertext links to those Web pages, and may be grouped into apredetermined number of (e.g., ten) search results.

The search engine 220 may submit a request for ads to the ad server120/210. The request may include a number of ads desired. This numbermay depend on the search results, the amount of screen or page spaceoccupied by the search results, the size and shape of the ads, etc. Inone embodiment, the number of desired ads will be from one to ten, andpreferably from three to five. The request for ads may also include thequery (as entered or parsed), information based on the query (such asgeolocation information, whether the query came from an affiliate and anidentifier of such an affiliate), and/or information associated with, orbased on, the search results. Such information may include, for example,identifiers related to the search results (e.g., document identifiers or“docIDs”), scores related to the search results (e.g., informationretrieval (“IR”) scores such as dot products of feature vectorscorresponding to a query and a document, Page Rank scores, and/orcombinations of IR scores and Page Rank scores), snippets of textextracted from identified documents (e.g., Web pages), full text ofidentified documents, topics of identified documents, feature vectors ofidentified documents, etc.

The search engine 220 may combine the search results with one or more ofthe advertisements provided by the ad server 120/210. This combinedinformation including the search results and advertisement(s) is thenforwarded towards the user that submitted the search, for presentationto the user. Preferably, the search results are maintained as distinctfrom the ads, so as not to confuse the user between paid advertisementsand presumably neutral search results.

Finally, the search engine 220 may transmit information about the ad andwhen, where, and/or how the ad was to be rendered (e.g., position,click-through or not, impression time, impression date, size, conversionor not, etc.) back to the ad server 120/210. Alternatively, or inaddition, such information may be provided back to the ad server 120/210by some other means.

Finally, the e-mail server 240 may be thought of, generally, as acontent server in which a document served is simply an e-mail. Further,e-mail applications (such as Microsoft Outlook for example) may be usedto send and/or receive e-mail. Therefore, an e-mail server 240 orapplication may be thought of as an ad consumer 130. Thus, e-mails maybe thought of as documents, and targeted ads may be served inassociation with such documents. For example, one or more ads may beserved in, under over, or otherwise in association with an e-mail.

Although the foregoing examples described servers as (i) requesting ads,and (ii) combining them with content, one or both of these operationsmay be performed by a client device (such as an end user computer forexample).

§ 4.1.2 Definitions

Online ads, such as those used in the exemplary systems described abovewith reference to FIGS. 1 and 2, or any other system, may have variousintrinsic features. Such features may be specified by an applicationand/or an advertiser. These features are referred to as “ad features”below. For example, in the case of a text ad, ad features may include atitle line, ad text, and an embedded link. In the case of an image ad,ad features may include images, executable code, and an embedded link.Depending on the type of online ad, ad features may include one or moreof the following: text, a link, an audio file, a video file, an imagefile, executable code, embedded information, etc.

When an online ad is served, one or more parameters may be used todescribe how, when, and/or where the ad was served. These parameters arereferred to as “serving parameters” below. Serving parameters mayinclude, for example, one or more of the following: features of(including information on) a page on which the ad was served, a searchquery or search results associated with the serving of the ad, a usercharacteristic (e.g., their geographic location, the language used bythe user, the type of browser used, previous page views, previousbehavior), a host or affiliate site (e.g., America Online, Google,Yahoo) that initiated the request, an absolute position of the ad on thepage on which it was served, a position (spatial or temporal) of the adrelative to other ads served, an absolute size of the ad, a size of thead relative to other ads, a color of the ad, a number of other adsserved, types of other ads served, time of day served, time of weekserved, time of year served, etc. Naturally, there are other servingparameters that may be used in the context of the invention.

Although serving parameters may be extrinsic to ad features, they may beassociated with an ad as serving conditions or constraints. When used asserving conditions or constraints, such serving parameters are referredto simply as “serving constraints” (or “targeting criteria”). Forexample, in some systems, an advertiser may be able to target theserving of its ad by specifying that it is only to be served onweekdays, no lower than a certain position, only to users in a certainlocation, etc. As another example, in some systems, an advertiser mayspecify that its ad is to be served only if a page or search queryincludes certain keywords or phrases. As yet another example, in somesystems, an advertiser may specify that its ad is to be served only if adocument being served includes certain topics or concepts, or fallsunder a particular cluster or clusters, or some other classification orclassifications.

“Ad information” may include any combination of ad features, ad servingconstraints, information derivable from ad features or ad servingconstraints (referred to as “ad derived information”), and/orinformation related to the ad (referred to as “ad related information”),as well as an extension of such information (e.g., information derivedfrom ad related information).

A “document” is to be broadly interpreted to include anymachine-readable and machine-storable work product. A document may be afile, a combination of files, one or more files with embedded links toother files, etc. The files may be of any type, such as text, audio,image, video, etc. Parts of a document to be rendered to an end user canbe thought of as “content” of the document. A document may include“structured data” containing both content (words, pictures, etc.) andsome indication of the meaning of that content (for example, e-mailfields and associated data, HTML tags and associated data, etc.) Adspots in the document may be defined by embedded information orinstructions. In the context of the Internet, a common document is a Webpage. Web pages often include content and may include embeddedinformation (such as meta information, hyperlinks, etc.) and/or embeddedinstructions (such as Javascript, etc.). In many cases, a document has aunique, addressable, storage location and can therefore be uniquelyidentified by this addressable location. A universal resource locator(URL) is a unique address used to access information on the Internet.

“Document information” may include any information included in thedocument, information derivable from information included in thedocument (referred to as “document derived information”), and/orinformation related to the document (referred to as “document relatedinformation”), as well as an extensions of such information (e.g.,information derived from related information). An example of documentderived information is a classification based on textual content of adocument. Examples of document related information include documentinformation from other documents with links to the instant document, aswell as document information from other documents to which the instantdocument links.

Content from a document may be rendered on a “content renderingapplication or device”. Examples of content rendering applicationsinclude an Internet browser (e.g., Explorer or Netscape), a media player(e.g., an MP3 player, a Realnetworks streaming audio file player, etc.),a viewer (e.g., an Abobe Acrobat pdf reader), etc.

A “content owner” is a person or entity that has some property right inthe content of a document. A content owner may be an author of thecontent. In addition, or alternatively, a content owner may have rightsto reproduce the content, rights to prepare derivative works of thecontent, rights to display or perform the content publicly, and/or otherproscribed rights in the content. Although a content server might be acontent owner in the content of the documents it serves, this is notnecessary.

“User information” may include user behavior information and/or userprofile information.

“E-mail information” may include any information included in an e-mail(also referred to as “internal e-mail information”), informationderivable from information included in the e-mail and/or informationrelated to the e-mail, as well as extensions of such information (e.g.,information derived from related information). An example of informationderived from e-mail information is information extracted or otherwisederived from search results returned in response to a search querycomposed of terms extracted from an e-mail subject line. Examples ofinformation related to e-mail information include e-mail informationabout one or more other e-mails sent by the same sender of a givene-mail, or user information about an e-mail recipient. Informationderived from or related to e-mail information may be referred to as“external e-mail information.”

Various exemplary embodiments of the present invention are now describedin § 4.2.

§ 4.2 Exemplary Embodiments

Two different implementations of the present invention are describedbelow. In the first, broad ad blocking is applied after an initial setof content-relevant ads is determined. In the second, broad ad blockingis used to affect an initial determination of a set of content-relevantads.

FIG. 3 is a bubble diagram of a first embodiment in which a set ofcandidate ads is filtered in a manner consistent with the presentinvention. A partner document (e.g., a Web page) 310 includes content315. A content targeted ad server (not shown) may be used to generate aset 320 of candidate ads. Each of the ads may include ad information322. The ad information 322 may include one or more of ad relevanceinformation 324, ad targeting information 326 and ad creative content328. The ad information 322 may also include a link (not shown) to alanding page. The ad relevance information 324 may include one or moresemantic clusters, such as probabilistic hierarchical inferentiallearner (PHIL) clusters (See Provisional Application Ser. No. 60/416,144(incorporated herein by reference), titled “METHODS AND APPARATUS FORPROBABILISTIC HIERARCHICAL INFERENTIAL LEARNER,” filed on Oct. 30, 2002and listing Georges Harik and Noam Shazeer as inventors.), for example.A “semantic cluster” may be a collection or group of words or symbolshaving some relationship. For example, documents (or even searchqueries, or sentences, or passages, etc.) with the word “car” may ofteninclude the terms “lease,” “dealer,” “new,” “used,” and “previouslyowned.” Therefore, these terms may be included in (at least one) givensemantic cluster. The ad relevance information may have been specified,and/or may have been determined from ad creative content, content of thelanding page, etc.

Filter information 340 may include a property name 342, broad ad blockinformation 344, and/or specific ad block information 346. The propertyname 342 is used to identify one or more documents (e.g., Web pages)with which the broad and/or specific ad block information is to be used.The property name 342 may identify an entire domain (e.g., an entireWebsite), a path (e.g., a URL of a particular Web page), etc. The broadad block information 344 may include categories of ads to block when adocument covered by the specified property 342 is served. The specificad block information 346 may include terms (i.e., words and/or phrases)which, if found in an ad, blocks the serving of the ad with a documentcovered by the specified property 342. Exemplary data structures forstoring the filter information 340 are described in § 4.2.2 below.

Broad filtering operations 330 may use broad ad block information 344and ad relevance information 324 to generate a sub-set 350 of candidateads from the initial set 320 of candidate ads. Narrow filteringoperations 360 may use specific ad block information 346 and adtargeting information 326, ad creative content 328 and/or landing pagecontent (not shown) to generate a filtered set 370 of candidate ads fromthe sub-set 350 of candidate ads. Exemplary methods and apparatus forperforming broad filtering operations 330 and narrow filteringoperations 360 are described in § 4.2.1 below.

FIG. 4 is a bubble diagram of a second embodiment in which blocks andfilters ads in a manner consistent with the present invention. Similarto the block information 340 of FIG. 3, block information 440 mayinclude a property name 442, broad ad block information 444, and/orspecific ad block information 446. The property name 442 is used toidentify one or more documents (e.g., Web pages) with which the broadand/or specific ad block information is to be used. The property name442 may identify an entire domain, a path, etc. The broad ad blockinformation 444 may include categories of ads to block when a documentcovered by the specified property 442 is served. The specific ad blockinformation 446 may include words and/or phrases which, if found in anad, blocks the serving of the ad with a document covered by thespecified property 442. Exemplary data structures for storing the filterinformation 440 are described in § 4.2.2 below.

Relevance comparison operations 450 may be used to determine candidateads 470 using document relevance information 414 and ad relevanceinformation 424 for various ads. The document relevance information 414and ad relevance information 424 may have been extracted or generatedfrom document information 432 and ad information 434, respectively. Therelevance comparison operations 450 may use one or more comparisonfunctions. The comparison functions may use tunable parameters 455.Comparison function parameter adjustment operations 460 may adjustcomparison function parameters 455 using, at least, broad ad blockinformation 444. Filtering operations 490 may generate a filtered set495 of candidate ads (or ad groups) from the candidate ads (or adgroups) 470 using, at least, ads (or ad groups) 485. The ads (or adgroups) 485 may be determined by ad (or ad group) block determinationoperations 480 using, at least, specific ad block information 446.Exemplary methods and apparatus for performing comparison functionparameter adjustment operations 460, ad (group) block determinationoperations 480, and filtering operations 490 are described in § 4.2.1below.

Referring back to both FIGS. 3 and 4, although both broad and specificad blocking are illustrated as being used together, either could be usedwithout the other.

§ 4.2.1 Exemplary Methods

FIG. 5 is a flow diagram of an exemplary method 500 for performing broadad filtering (Recall, e.g., operation 330 of FIG. 3.) in a mannerconsistent with the present invention. Candidate ad information andbroad ad block information is accepted. (Block 510) Recall that broad adblock information may include categories of ads to block. A number ofacts are performed for each candidate ad (or until a number of ads areaccepted) as indicated by loop 520-570. As indicated by loop 530-560,for each category to be blocked, it is determined whether or not thecandidate ad belongs to the blocked category. (Decision block 540). Ifso, the ad is removed from the set of candidate ads (or not added to anext set of ads) (Block 550), before the method 500 continues to processa next candidate ad. Once all candidate ads have been processed, themethod 500 is left. (Node 580).

In an alternative to method 500, ads can be grouped in accordance withthe categories to which they belong. A given ad could belong to morethan one category group. If a category group corresponds to a blockedcategory, all ads belonging to that category group would be removed. Inan alternative to method 500, decision block 540 determines whether ornot an ad belongs to a predetermined number (e.g., 1-3) of blockedcategories before it is blocked.

FIG. 6 is a flow diagram of an exemplary method 600 for performingnarrow ad filtering (Recall, e.g., operations 360, 480 and 490 of FIGS.3 and 4.) in a manner consistent with the present invention. Candidatead sub-set information and specific ad block information is accepted.(Block 610) Recall that specific ad block information may includespecific terms. As indicated by loop 620-670, a number of acts areperformed for each candidate ad. As indicated by loop 630-660, for eachterm to be blocked, it is determined whether ad information (or aparticular part or parts of ad information) includes the term to beblocked. (Decision block 640) If so, the ad is removed from the sub-setof candidate ads (or not added to a next set of ads) (Block 650), beforethe method 600 continues to process a next candidate ad. Once allcandidate ads have been processed, the method 600 is left. (Node 680).Referring back to decision block 640 ad information may include one ormore of: (i) serving constraints such as targeting keywords; (ii) adcreative content, (iii) landing page content, etc.

In an alternative to method 600, the decision block 640 may determinewhether the term to be blocked is used at least a predetermined numberof times in the ad information.

FIG. 7 is a flow diagram of an exemplary method 700 for performing broadad blocking (Recall, e.g., 450, 455 and 460 of FIG. 4.) in a mannerconsistent with the present invention. Broad ad block information isaccepted. (Block 710) As indicated by loop 720-740, for each ad categoryto block, the weight of the category used in a relevance comparisonfunction is adjusted (e.g., to zero). (Block 730). The ad relevance anddocument relevance information are accepted (Block 750) and the documentis compared with each ad using the relevance information and themodified relevance comparison function (Block 760) before the method 700is left (Node 770). In one embodiment, the document-ad relevancecomparison generates a similarity measure.

§ 4.2.2 Exemplary Data Structures

The ad blocking information 340, 440 may be referred to as “catlists.”Catlists can be specified for each new ad category that needs to beblocked for a particular property. It may contain the followinginformation, perhaps in a text file:

-   -   Property name (e.g., “ca-wunderground”)    -   Domain and/or path restriction (e.g.,        http://www.wunderground.com/US/CA) (optional)    -   “Broad” terms relevant to the category of ads that will be        blocked. For example, if a popular Website has an exclusive        advertising arrangement with Phillips for consumer electronics,        the broad terms might include “electronics” or “appliances” or        “electric razors”. The broad terms may specify a minimum number        of broad terms that need to match before an ad is blocked.        Alternatively, a partner could simply specify an objectionable        ad or an objectionable Web page or Website from which category        information, and therefore broad terms, could be derived.    -   “Specific” terms relevant to the text (e.g., creative text,        keyword targeting criteria, etc.) of an ad that should be        blocked. For example, to block ads for Sony televisions, the        specific terms might include “sony televisions”, “sony wega”,        “sony hdtv”, etc. Ads with creative text, keyword targeting        criteria, and/or landing page text, etc. that contain these        terms would be blocked. The specific terms may specify a minimum        number of specific terms that need to match before an ad is        blocked.        The catlists may be entered into a text representation (e.g., a        file) containing a list of catlist entries.

In one embodiment of the present invention, this text file may beprovided to an application which may parse the above information andgenerate a data structure that associates (e.g., maps) theproperties/domains with semantic clusters (e.g., PHIL clusters) for“broad” terms and associates the properties/domains with ads (or adgroups) for “specific” terms, to generate semantic clusters of ads, aswell as ads (or ad groups) that should be blocked. The content-targetedad server may load this data structure into memory. The data structure(which could be XML in an alternative embodiment) may look somethinglike the following: parsed message CatlistTable { repeated group Entry {required string property; optional string url_restriction; repeated intblocked_cluster_ids; repeated int blocked_adgroup_ids; } }In a more general embodiment, the cluster_ids may be any conceptidentifier. Similarly, in a more general embodiment, the adgroup_ids maybe any ad identifier. Ads may be blocked at run time. For example, thefile containing this data structure may be passed to the acontent-targeted ad server, which may load it into a data structure atstartup (or whenever that file is changed). This data structure may bekeyed off the property and url_restriction. For example, during an adsmatching phase, the content-relevant ad server may reduce the weight ofsemantic (e.g., PHIL) clusters that are in blocked_cluster_ids to zero.The remaining clusters may then used to generate a list of ads (or adgroups) applicable to the document. If an ad has (or if an ad groupincludes an ad that has) creative text, keyword targeting criteria, etc.matching the “specific” terms then it will be removed fromconsideration. For example, the content-targeting ad server may scanremaining ads (or ad groups) and remove those with ids are inblocked_adgroup_ids.

§ 4.2.3 Exemplary Apparatus

FIG. 11 is high-level block diagram of a machine 1100 that may performone or more of the operations discussed above. The machine 1100basically includes one or more processors 1110, one or more input/outputinterface units 1130, one or more storage devices 1120, and one or moresystem buses and/or networks 1140 for facilitating the communication ofinformation among the coupled elements. One or more input devices 1132and one or more output devices 1134 may be coupled with the one or moreinput/output interfaces 1130.

The one or more processors 1110 may execute machine-executableinstructions (e.g., C or C++ running on the Solaris operating systemavailable from Sun Microsystems Inc. of Palo Alto, Calif. or the Linuxoperating system widely available from a number of vendors such as RedHat, Inc. of Durham, N.C.) to effect one or more aspects of the presentinvention. At least a portion of the machine executable instructions maybe stored (temporarily or more permanently) on the one or more storagedevices 1120 and/or may be received from an external source via one ormore input interface units 1130.

In one embodiment, the machine 1100 may be one or more conventionalpersonal computers. In this case, the processing units 1110 may be oneor more microprocessors. The bus 1140 may include a system bus. Thestorage devices 1120 may include system memory, such as read only memory(ROM) and/or random access memory (RAM). The storage devices 1120 mayalso include a hard disk drive for reading from and writing to a harddisk, a magnetic disk drive for reading from or writing to a (e.g.,removable) magnetic disk, and an optical disk drive for reading from orwriting to a removable (magneto-) optical disk such as a compact disk orother (magneto-) optical media.

A user may enter commands and information into the personal computerthrough input devices 1132, such as a keyboard and pointing device(e.g., a mouse) for example. Other input devices such as a microphone, ajoystick, a game pad, a satellite dish, a scanner, or the like, may also(or alternatively) be included. These and other input devices are oftenconnected to the processing unit(s) 1110 through an appropriateinterface 1130 coupled to the system bus 1140. The output devices 1134may include a monitor or other type of display device, which may also beconnected to the system bus 1140 via an appropriate interface. Inaddition to (or instead of) the monitor, the personal computer mayinclude other (peripheral) output devices (not shown), such as speakersand printers for example.

§ 4.2.4 Alternatives

The above mechanism could also be to automatically support “channels” insearch-based targeting. For example, a partner may buy up an entirecategory such as “flowers” and the above system can be used toautomatically restrict advertisers who buy related keywords such as“tulips” or “violets.”

§ 4.3 Illustrative Examples of Operations of Exemplary Embodiments

Examples of broad ad blocking using two different embodiments of thepresent invention are described with reference to FIGS. 8 and 9. Then,an example of specific ad blocking is described with reference to FIG.10. In this example, assume that weather ads are to be blocked forwww.wunderground.com pages that are for cities in California. Further,assume that the property has an exclusive advertising relationship withCoppertone for sunblock and suntan lotion. Ad blocking information maybe defined as follows (where the pound sign “#” indicates a commentline):

Begin

-   # Property name-   property: ca-wunderground-   # domain or path restriction (ads will be blocked only for docs with    this URL prefix)-   urlrestriction: http://www.wunderground.com/US/CA-   # “Broad terms”-   # broad:<threshold>:<words>repeated-   # words—words relevant to the category of ads that need to be    blocked-   # threshold—number of matches of words in a phil cluster with words-   # in words before the cluster is no longer used to determine what    ads-   # will be shown-   broad: 3: weather weather-forecast forecast temperature-   broad: 2: meteorology meteorological-   # “Specific terms”-   # specific:<threshold>:<words>repeated-   # words—words relevant to specific text within ads that need to be    blocked-   # threshold—number of matches of words in ad creative or criteria    text with words-   # in words before the ad group containing the ad is no longer shown-   specific: 1: anemometer-   specific: 1: sunscreen, suntan, tan, UV-   specific: 2: wind speed-   specific: 2: weather instrument    End

FIG. 8 illustrates an application of a broad ad filtering technique thatis consistent with the present invention. The ad blocking informationjust discussed is shown as block 820, including the broad ad blockinginformation 822 and the specific ad blocking information 824. The Webpage document 810 is associated with the blocking information 820.Notice that the Web page document 810 may include a number of terms andconcepts. In this example, the terms or concepts may be related to thegeographic region the weather is being reported on (e.g., Lake Tahoe,Nevada, California, etc.), weather (e.g., temperature, humidity, windsunny, cloudy, clear, overcast, rain, snow, sleet, hail, etc.), outdoorconditions (e.g., UV index, pollen count, etc.) and conditions relatedto various outdoor activities (e.g., skiing conditions, powder,granular, base, boating conditions, high tide, low tide, swell, etc.). Anumber (which may be subject to a predetermined limit) of the mostrelevant concepts of the Web page document 810 may be extracted. Asshown, in this example, the most relevant concepts 830 may includeweather, lake tahoe, temperature, ski, boat, and allergies. Acontent-targeted ad server (such as those introduced in § 1.2 above)generates a set of content-relevant ads, the concepts of which aredepicted in block 840. Each of the ads may be associated with one ormore of the concepts. Broad filtering operations 850 generate a revisedset of content-relevant ads, the concepts of which are depicted in block860, from the initial set of content-relevant ads using, at least, thebroad ad blocking information 822. As shown, ads associated with theconcepts weather or temperature have been removed from consideration.

FIG. 9 illustrates an application of a broad ad blocking technique thatis consistent with the present invention. As was the case with theembodiment exemplified in FIG. 8, the ad blocking information 920includes the broad ad blocking information 922 and the specific adblocking information 924. The Web page document 910 is associated withthe blocking information 920 and may include a number of terms andconcepts. A number (which may be subject to a predetermined limit) ofthe most relevant concepts 930 of the Web page document 910 may beextracted. Broad ad filtering operations 950 may modify a comparisonpart (not shown) of a content-targeted ad server (such as thoseintroduced in § 1.2 above) using, at least, the broad ad blockinginformation 922. The content-targeted ad server may then generate a setof content-relevant ads, the concepts of which are depicted in block960, where ads associated with the concepts weather or temperature havebeen removed from consideration.

FIG. 10 illustrates an application of a specific ad filtering techniquethat is consistent with the present invention. A revised set ofcontent-relevant ads, the concepts of which are depicted in block860/960, may include a number of ads, such as text ads 1010. Thespecific ad filtering operations 1020 may filter these ads 1010, usingat least the terms specified by the specific ad blocking information824/924, to generate a reduced set 1030 of ads. Notice that the “blockit sunscreen” ad was blocked because its creative text included the term“sunscreen,” and because its targeting keywords included the terms“sunscreen” and “UV.” The “ultra goggles” ad was also blocked becauseits creative included the term “UV.”

§ 4.4 CONCLUSIONS

In view of the foregoing, the present invention teaches improved adblocking techniques. Such techniques (i) are easy to set up, (ii) areeasy to manage, (iii) avoid under-inclusion, and/or (iv) avoidover-inclusion. These techniques work well with content-targeting adserving systems. The present invention may be used to fine tune the adblocking depending on course, and/or fine grain category definitions(e.g. course: car sales, fine: new car sales). Finally, the blocking canoccur at either the property level (e.g. Yahoo) the domain level (e.g.shopping.yahoo.com), or the path level (e.g.shopping.yahoo.com/flowers). Various aspect of the present invention maybe used alone, together, and/or together without ad blocking techniques.

1. A method for blocking advertisements, the method comprising: a)accepting at least one category of ads to be blocked; b) accepting atleast one ad, each ad being associated with at least one category; andc) preventing an ad from being served if at least a predetermined numberof its at least one category match any of the at least one category ofads to be blocked.
 2. The method of claim 1 wherein the category is asemantic cluster.
 3. The method of claim 1 wherein the category is aprobabilistic hierarchical inferential learner cluster.
 4. The method ofclaim 1 wherein the category is a concept.
 5. The method of claim 1wherein the act of preventing an ad from being served includes removingthe ad from a set of eligible ads.
 6. The method of claim 1 wherein thepredetermined number is one.
 7. The method of claim 1 wherein at leastone of the at least one category is a product category.
 8. The method ofclaim 1 wherein at least one of the at least one category is a servicecategory.
 9. The method of claim 1 wherein the at least one category ofads to be blocked is accepted from a list associated with at least onedocument.
 10. The method of claim 9 wherein the at least one document isat least one Web page.
 11. The method of claim 9 wherein the at leastone document include Web pages of a Website.
 12. The method of claim 9wherein the at least one document is at least one Web page associatedwith a path name.
 13. The method of claim 1 further comprising: d)accepting at least one term; and e) preventing an ad from being servedif at least a part of its ad information includes at least a secondpredetermined number of any of the at least one term.
 14. The method ofclaim 13 wherein the at least a part of the ad information is content ofa creative of the ad.
 15. The method of claim 13 wherein the at least apart of the ad information is keyword targeting terms associated withthe ad.
 16. The method of claim 13 wherein the at least a part of the adinformation is content of a document linked to by the ad.
 17. The methodof claim 13 wherein the second predetermined number is one.
 18. Themethod of claim 13 wherein at least one of the at least one term is aproduct name.
 19. The method of claim 13 wherein at least one of the atleast one term is a name of a product manufacturer.
 20. The method ofclaim 13 wherein at least one of the at least one term is a name of aproduct retailer.
 21. The method of claim 13 wherein at least one of theat least one term is a service name.
 22. The method of claim 13 whereinat least one of the at least one term is a name of a service provider.23. The method of claim 13 wherein the at least one term is acceptedfrom a list associated with at least one document.
 24. The method ofclaim 23 wherein the at least one document is at least one Web page. 25.The method of claim 23 wherein the at least one document include Webpages of a Website.
 26. The method of claim 23 wherein the at least onedocument is at least one Web page associated with a path name.
 27. Amethod for determining a set of advertisements, the method comprising:a) accepting at least one category of ads to be blocked; b) adjusting aweight of each of the at least one category in a comparison function; c)accepting at least one ad, each ad being associated with at least onecategory; d) accepting a document being associated with at leastcategory; and e) comparing each of the at least one ad with the documentusing the comparison function.
 28. The method of claim 27 wherein thecategory is a semantic cluster.
 29. The method of claim 27 wherein thecategory is a probabilistic hierarchical inferential learner cluster.30. The method of claim 27 wherein the category is a concept.
 31. Themethod of claim 27 wherein the act of adjusting a weight of each of theat least one category in a comparison function includes setting theweight to zero.
 32. The method of claim 27 wherein at least one of theat least one category is a product category.
 33. The method of claim 27wherein at least one of the at least one category is a service category.34. The method of claim 27 wherein the at least one category of ads tobe blocked is accepted from a list associated with at least onedocument.
 35. The method of claim 34 wherein the at least one documentis at least one Web page.
 36. The method of claim 34 wherein the atleast one document include Web pages of a Website.
 37. The method ofclaim 34 wherein the at least one document is at least one Web pageassociated with a path name.
 38. The method of claim 27 furthercomprising: f) accepting at least one term; and g) preventing an ad frombeing served if at least a part of its ad information includes at leasta second predetermined number of any of the at least one term.
 39. Themethod of claim 38 wherein the at least a part of the ad information iscontent of a creative of the ad.
 40. The method of claim 38 wherein theat least a part of the ad information is keyword targeting termsassociated with the ad.
 41. The method of claim 38 wherein the at leasta part of the ad information is content of a document linked to by thead.
 42. The method of claim 38 wherein the second predetermined numberis one.
 43. The method of claim 38 wherein at least one of the at leastone term is a product name.
 44. The method of claim 38 wherein at leastone of the at least one term is a name of a product manufacturer. 45.The method of claim 38 wherein at least one of the at least one term isa name of a product retailer.
 46. The method of claim 38 wherein atleast one of the at least one term is a service name.
 47. The method ofclaim 38 wherein at least one of the at least one term is a name of aservice provider.
 48. The method of claim 38 wherein the at least oneterm is accepted from a list associated with at least one document. 49.The method of claim 48 wherein the at least one document is at least oneWeb page.
 50. The method of claim 48 wherein the at least one documentinclude Web pages of a Website.
 51. The method of claim 48 wherein theat least one document is at least one Web page associated with a pathname.
 52. Apparatus for blocking advertisements, the apparatuscomprising: a) an input for accepting: i) at least one category of adsto be blocked, and ii) at least one ad, each ad being associated with atleast one category; and b) means for preventing an ad from being servedif at least a predetermined number of its at least one category matchany of the at least one category of ads to be blocked.
 53. The apparatusof claim 52 wherein the category is a semantic cluster.
 54. Theapparatus of claim 52 wherein the category is a probabilistichierarchical inferential learner cluster.
 55. The apparatus of claim 52wherein the category is a concept.
 56. The apparatus of claim 52 whereinthe means for preventing an ad from being served includes means forremoving the ad from a set of eligible ads.
 57. The apparatus of claim52 wherein the predetermined number is one.
 58. The apparatus of claim52 wherein at least one of the at least one category is a productcategory.
 59. The apparatus of claim 52 wherein at least one of the atleast one category is a service category.
 60. The apparatus of claim 52wherein the at least one category of ads to be blocked is accepted froma list associated with at least one document.
 61. The apparatus of claim60 wherein the at least one document is at least one Web page.
 62. Theapparatus of claim 60 wherein the at least one document include Webpages of a Website.
 63. The apparatus of claim 60 wherein the at leastone document is at least one Web page associated with a path name. 64.The apparatus of claim 60 wherein the input if further adapted to acceptat least one term, the apparatus further comprising: c) preventing an adfrom being served if at least a part of its ad information includes atleast a second predetermined number of any of the at least one term. 65.The apparatus of claim 64 wherein the at least a part of the adinformation is content of a creative of the ad.
 66. The apparatus ofclaim 64 wherein the at least a part of the ad information is keywordtargeting terms associated with the ad.
 67. The apparatus of claim 64wherein the at least a part of the ad information is content of adocument linked to by the ad.
 68. The apparatus of claim 64 wherein thesecond predetermined number is one.
 69. The apparatus of claim 64wherein at least one of the at least one term is a product name.
 70. Theapparatus of claim 64 wherein at least one of the at least one term is aname of a product manufacturer.
 71. The apparatus of claim 64 wherein atleast one of the at least one term is a name of a product retailer. 72.The apparatus of claim 64 wherein at least one of the at least one termis a service name.
 73. The apparatus of claim 64 wherein at least one ofthe at least one term is a name of a service provider.
 74. The apparatusof claim 64 wherein the at least one term is accepted from a listassociated with at least one document.
 75. The apparatus of claim 64wherein the at least one document is at least one Web page.
 76. Theapparatus of claim 64 wherein the at least one document include Webpages of a Website.
 77. The apparatus of claim 64 wherein the at leastone document is at least one Web page associated with a path name. 78.Apparatus for determining a set of advertisements, the apparatuscomprising: a) an input for accepting i) at least one category of ads tobe blocked, ii) at least one ad, each ad being associated with at leastone category, and iii) a document being associated with at leastcategory; b) means for adjusting a weight of each of the at least onecategory in a comparison function; and c) means for comparing each ofthe at least one ad with the document using the comparison function. 79.The apparatus of claim 78 wherein the category is a semantic cluster.80. The apparatus of claim 78 wherein the category is a probabilistichierarchical inferential learner cluster.
 81. The apparatus of claim 78wherein the category is a concept.
 82. The apparatus of claim 78 whereinthe means for adjusting a weight of each of the at least one category ina comparison function includes setting the weight to zero.
 83. Theapparatus of claim 78 wherein at least one of the at least one categoryis a product category.
 84. The apparatus of claim 78 wherein at leastone of the at least one category is a service category.
 85. Theapparatus of claim 78 wherein the at least one category of ads to beblocked is accepted from a list associated with at least one document.86. The apparatus of claim 85 wherein the at least one document is atleast one Web page.
 87. The apparatus of claim 85 wherein the at leastone document include Web pages of a Website.
 88. The apparatus of claim85 wherein the at least one document is at least one Web page associatedwith a path name.
 89. The apparatus of claim 78 wherein the input isfurther adapted to accept at least one term, the apparatus furthercomprising: d) means for preventing an ad from being served if at leasta part of its ad information includes at least a second predeterminednumber of any of the at least one term.
 90. The apparatus of claim 89wherein the at least a part of the ad information is content of acreative of the ad.
 91. The apparatus of claim 89 wherein the at least apart of the ad information is keyword targeting terms associated withthe ad.
 92. The apparatus of claim 89 wherein the at least a part of thead information is content of a document linked to by the ad.
 93. Theapparatus of claim 89 wherein the second predetermined number is one.94. The apparatus of claim 89 wherein at least one of the at least oneterm is a product name.
 95. The apparatus of claim 89 wherein at leastone of the at least one term is a name of a product manufacturer. 96.The apparatus of claim 89 wherein at least one of the at least one termis a name of a product retailer.
 97. The apparatus of claim 89 whereinat least one of the at least one term is a service name.
 98. Theapparatus of claim 89 wherein at least one of the at least one term is aname of a service provider.
 99. The apparatus of claim 89 wherein the atleast one term is accepted from a list associated with at least onedocument.
 100. The apparatus of claim 99 wherein the at least onedocument is at least one Web page.
 101. The apparatus of claim 99wherein the at least one document include Web pages of a Website. 102.The apparatus of claim 99 wherein the at least one document is at leastone Web page associated with a path name.